# We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

arXiv: https://arxiv.org/abs/2105.14188 · HTML: https://mlsanity.com/paper/2105.14188/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Learning Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/1801.02294/) (2018)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https://mlsanity.com/paper/1805.10000/) (2018)
- [Algorithms for multi-armed bandit problems](https://mlsanity.com/paper/1402.6028/) (2014)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising](https://mlsanity.com/paper/1803.02194/) (2018)
- [Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising](https://mlsanity.com/paper/2006.16312/) (2020)
- [A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction](https://mlsanity.com/paper/2008.08931/) (2020)
- [Optimal Real-Time Bidding Frameworks Discussion](https://mlsanity.com/paper/1602.01007/) (2016)

## Built on by (most cited first, 5 of 5)

- [Lightweight Auto-bidding based on Traffic Prediction in Live Advertising](https://mlsanity.com/paper/2508.06069/) (2025)
- [Heterogeneous Influence Maximization in User Recommendation](https://mlsanity.com/paper/2508.13517/) (2025)
- [ALT: An Automatic System for Long Tail Scenario Modeling](https://mlsanity.com/paper/2305.11390/) (2023)
- [ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising](https://mlsanity.com/paper/2412.06167/) (2024)
- [Learn to Match: Two-Sided Matching with Temporally Extended Feedback](https://mlsanity.com/paper/2606.06744/) (2026)

## Abstract (arXiv)

Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial and error in discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives. In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising. We further augment this prototype system by explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization objectives through their adoptions of different recommending advertising strategies. We use contextual bandit algorithms to efficiently learn the advertisers' preferences and maximize the recommendation adoption, simultaneously. Simulation experiments based on Taobao online bidding data show that the designed algorithms can effectively optimize the strategy adoption rate of advertisers.